Wang Kaidong, Tan Bing, Wang Xinfei, Qiu Shicheng, Zhang Qiuping, Wang Shaolei, Yen Ying-Tzu, Jing Nan, Liu Changming, Chen Xuxu, Liu Shichang, Yu Yan
Division of Cardiology, Department of Medicine, David Geffen School of Medicine University of California Los Angeles Los Angeles California USA.
Department of Bioengineering, Henry Samueli School of Engineering and Applied Science University of California Los Angeles Los Angeles California USA.
Bioeng Transl Med. 2025 Feb 3;10(4):e70002. doi: 10.1002/btm2.70002. eCollection 2025 Jul.
Cardiovascular diseases (CVDs) continue to drive global mortality rates, underscoring an urgent need for advancements in healthcare solutions. The development of point-of-care (POC) devices that provide rapid diagnostic services near patients has garnered substantial attention, especially as traditional healthcare systems face challenges such as delayed diagnoses, inadequate care, and rising medical costs. The advancement of machine learning techniques has sparked considerable interest in medical research and engineering, offering ways to enhance diagnostic accuracy and relevance. Improved data interoperability and seamless connectivity could enable real-time, continuous monitoring of cardiovascular health. Recent breakthroughs in computing power and algorithmic design, particularly deep learning frameworks that emulate neural processes, have revolutionized POC devices for CVDs, enabling more frequent detection of abnormalities and automated, expert-level diagnosis. However, challenges such as data privacy concerns and biases in dataset representation continue to hinder clinical integration. Despite these barriers, the translational potential of machine learning-assisted POC devices presents significant opportunities for advancement in CVDs healthcare.
心血管疾病（CVDs）持续推动着全球死亡率上升，凸显了医疗保健解决方案取得进展的迫切需求。能够在患者身边提供快速诊断服务的即时检测（POC）设备的开发已引起了广泛关注，尤其是在传统医疗系统面临诸如诊断延迟、护理不足和医疗成本上升等挑战的情况下。机器学习技术的进步在医学研究和工程领域引发了极大兴趣，为提高诊断准确性和相关性提供了途径。改进的数据互操作性和无缝连接能够实现对心血管健康的实时、持续监测。计算能力和算法设计方面的最新突破，特别是模拟神经过程的深度学习框架，已经彻底改变了用于心血管疾病的即时检测设备，能够更频繁地检测异常并实现自动化的专家级诊断。然而，诸如数据隐私问题和数据集表示中的偏差等挑战仍然阻碍着临床整合。尽管存在这些障碍，机器学习辅助的即时检测设备的转化潜力为心血管疾病医疗保健的进步带来了重大机遇。
